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Record W4224086097 · doi:10.2166/wh.2022.014

Development of a qPCR assay for the detection of naturalized wastewater <i>E. coli</i> strains

2022· article· en· W4224086097 on OpenAlexafffund
Shuai Zhi, Graham Banting, Norman F. Neumann

Bibliographic record

VenueJournal of Water and Health · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicFecal contamination and water quality
Canadian institutionsUniversity of Alberta
FundersCity of CalgaryNatural Science Foundation of NingboAlberta InnovatesNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsWastewaterSewageEscherichia coliBiologyMicrobiologySewage treatmentPolymerase chain reactionEffluentPlasmidFecesContaminationHuman fecesFecal coliformDNAEnvironmental engineeringWater qualityEnvironmental scienceEcologyGeneticsGene

Abstract

fetched live from OpenAlex

Abstract We recently demonstrated the presence of naturalized populations of Escherichia coli in municipal sewage. We wanted to develop a quantitative polymerase chain reaction (qPCR) assay targeting the uspC-IS30-flhDC marker of naturalized wastewater E. coli and assess the prevalence of these naturalized strains in wastewater. The limit of detection for the qPCR assay was 3.0 × 10−8 ng of plasmid DNA template with 100% specificity. This strain was detected throughout the wastewater treatment process, including treated effluents. We evaluated the potential of this marker for detecting municipal sewage/wastewater contamination in water by comparing it to other human and animal markers of fecal pollution. Strong correlations were observed between the uspC-IS30-flhDC marker and the human fecal markers Bacteroides HF183 and HumM2, but not animal fecal markers, in surface and stormwater samples. The uspC-IS30-flhDC marker appears to be a potential E. coli-based marker for human wastewater contamination.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.200
Threshold uncertainty score0.155

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.042
GPT teacher head0.297
Teacher spread0.255 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2022
Admission routes2
Has abstractyes

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